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<h1 class="title toc-ignore">Clustering Practice</h1>
<h4 class="author"><em>weiya</em></h4>
<h4 class="date"><em>March 3, 2019</em></h4>

</div>


<div id="k-means" class="section level2">
<h2><span class="math inline">\(K\)</span>-means</h2>
<div class="figure">
<img src="images/alg_kmeans.png" />

</div>
<p>通过一个简单的例子来说明用 <code>kmeans()</code> 进行 <span class="math inline">\(K\)</span>-means 聚类。</p>
<pre class="r"><code>set.seed(123)
x = matrix(rnorm(50*2), ncol=2)
x[1:25, 1] = x[1:25, 1] + 3
x[1:25, 2] = x[1:25, 2] - 4</code></pre>
<p>然后进行 <span class="math inline">\(K\)</span>-means 聚类，</p>
<pre class="r"><code>km.out = kmeans(x, 2, nstart = 20)
plot(x, col = (km.out$cluster+1), main = &quot;K-means (K=2)&quot;, xlab = &quot;&quot;, ylab = &quot;&quot;, pch = 20, cex = 2)</code></pre>
<p><img src="cluster-practice_files/figure-html/unnamed-chunk-2-1.png" width="672" /></p>
<p>其中 <code>nstart</code> 表示随机初始划分的个数。可以比较下 <span class="math inline">\(K=3\)</span> 时 <code>nstart = 1</code> 和 <code>nstart = 20</code> 的差异</p>
<pre class="r"><code>set.seed(1234)
km.out = kmeans(x, 3, nstart = 1)
km.out$tot.withinss</code></pre>
<pre><code>## [1] 64.24859</code></pre>
<pre class="r"><code>km.out = kmeans(x, 3, nstart = 20)
km.out$tot.withinss</code></pre>
<pre><code>## [1] 62.43309</code></pre>
</div>
<div id="hierarchical-clustering" class="section level2">
<h2>Hierarchical Clustering</h2>
<div class="figure">
<img src="images/alg_hclust.png" />

</div>
<p>可以采用不同的 linkage 进行 <strong>系统聚类 (Hierarchical Clustering)</strong></p>
<div class="figure">
<img src="images/tab_hclust_linkage.png" />

</div>
<pre class="r"><code>hc.complete = hclust(dist(x), method = &quot;complete&quot;)
hc.average = hclust(dist(x), method = &quot;average&quot;)
hc.single = hclust(dist(x), method = &quot;single&quot;)</code></pre>
<p>并且作出 dendrograms.</p>
<pre class="r"><code>par(mfrow=c(1,3))
plot(hc.complete, main = &quot;Complete Linkage&quot;, xlab = &quot;&quot;, sub = &quot;&quot;, cex=.9)
plot(hc.average, main = &quot;Average Linkage&quot;, xlab = &quot;&quot;, sub = &quot;&quot;, cex=.9)
plot(hc.single, main = &quot;Single Linkage&quot;, xlab = &quot;&quot;, sub = &quot;&quot;, cex=.9)</code></pre>
<p><img src="cluster-practice_files/figure-html/unnamed-chunk-5-1.png" width="672" /></p>
<p>为了确定分类结果，可以用 <code>cutree()</code> 返回类别标签，</p>
<pre class="r"><code>cutree(hc.complete, 2)</code></pre>
<pre><code>##  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2
## [36] 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2</code></pre>
<p>当使用基于相关系数的距离度量时，可以采用 <code>as.dist()</code> 函数，比如</p>
<pre class="r"><code>x = matrix(rnorm(30*3), ncol = 3)
dd = as.dist(1-cor(t(x)))
plot(hclust(dd, method=&quot;complete&quot;), main = &quot;Complete Linkage with Correlation-Based Distance&quot;, xlab = &quot;&quot;, sub = &quot;&quot;)</code></pre>
<p><img src="cluster-practice_files/figure-html/unnamed-chunk-7-1.png" width="672" /></p>
</div>
<div id="references" class="section level2">
<h2>References</h2>
<p><a href="https://www-bcf.usc.edu/~gareth/ISL/ISLR%20First%20Printing.pdf">James, G., Witten, D., Hastie, T., &amp; Tibshirani, R. (Eds.). (2013). An introduction to statistical learning: with applications in R. New York: Springer.</a></p>
</div>

<p>Copyright &copy; 2016-2019 weiya</p>



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